Score boosting strategies for capturing domain-specific biases in anomaly detection systems

ABSTRACT

In one embodiment, a device in a network detects an anomaly in the network using an anomaly detector. The anomaly corresponds to an anomalous behavior exhibited by one or more nodes in the network. The device computes an anomaly score for the anomaly that represents a measure of the anomalous behavior. The device adjusts the anomaly score using a boost score. The boost score is generated by a boosting function that accounts for domain-specific biases of the anomaly detector. The device reports the anomaly to a supervisory device based on whether the adjusted anomaly score exceeds a reporting threshold.

RELATED APPLICATIONS

This application claims priority to U.S. Provisional Application No.62/312,754, filed Mar. 24, 2016, entitled “SCORE BOOSTING STRATEGIES FORCAPTURING DOMAIN-SPECIFIC BIASES IN ANOMALY DETECTION SYSTEMS,” byVasseur et al., the contents of which are hereby incorporated byreference.

TECHNICAL FIELD

The present disclosure relates generally to computer networks, and, moreparticularly, to score boosting strategies for capturing domain-specificbiases in anomaly detection systems.

BACKGROUND

Enterprise networks are carrying a very fast growing volume of bothbusiness and non-business critical traffic. Often, business applicationssuch as video collaboration, cloud applications, etc., use the samehypertext transfer protocol (HTTP) and/or HTTP secure (HTTPS) techniquesthat are used by non-business critical web traffic. This complicates thetask of optimizing network performance for specific applications, asmany applications use the same protocols, thus making it difficult todistinguish and select traffic flows for optimization.

One type of network attack that is of particular concern in the contextof computer networks is a Denial of Service (DoS) attack. In general,the goal of a DoS attack is to prevent legitimate use of the servicesavailable on the network. For example, a DoS jamming attack mayartificially introduce interference into the network, thereby causingcollisions with legitimate traffic and preventing message decoding. Inanother example, a DoS attack may attempt to overwhelm the network'sresources by flooding the network with requests, to prevent legitimaterequests from being processed. A DoS attack may also be distributed, toconceal the presence of the attack. For example, a distributed DoS(DDoS) attack may involve multiple attackers sending malicious requests,making it more difficult to distinguish when an attack is underway. Whenviewed in isolation, a particular one of such a request may not appearto be malicious. However, in the aggregate, the requests may overload aresource, thereby impacting legitimate requests sent to the resource.

Botnets represent one way in which a DDoS attack may be launched againsta network. In a botnet, a subset of the network devices may be infectedwith malicious software, thereby allowing the devices in the botnet tobe controlled by a single master. Using this control, the master canthen coordinate the attack against a given network resource.

BRIEF DESCRIPTION OF THE DRAWINGS

The embodiments herein may be better understood by referring to thefollowing description in conjunction with the accompanying drawings inwhich like reference numerals indicate identically or functionallysimilar elements, of which:

FIGS. 1A-1B illustrate an example communication network;

FIG. 2 illustrates an example network device/node;

FIG. 3 illustrates an example self learning network (SLN)infrastructure;

FIG. 4 illustrates an example distributed learning agent (DLA);

FIGS. 5A-5D illustrate an example of a DLA applying a boost score to ananomaly score;

FIGS. 6A-6D illustrate an example of the distribution of a boostingfunction in a network; and

FIG. 7 illustrates an example simplified procedure for employing a scoreboosting strategy to capture domain-specific biases in an anomalydetection system.

DESCRIPTION OF EXAMPLE EMBODIMENTS Overview

According to one or more embodiments of the disclosure, a device in anetwork detects an anomaly in the network using an anomaly detector. Theanomaly corresponds to an anomalous behavior exhibited by one or morenodes in the network. The device computes an anomaly score for theanomaly that represents a measure of the anomalous behavior. The deviceadjusts the anomaly score using a boost score. The boost score isgenerated by a boosting function that accounts for domain-specificbiases of the anomaly detector. The device reports the anomaly to asupervisory device based on whether the adjusted anomaly score exceeds areporting threshold.

Description

A computer network is a geographically distributed collection of nodesinterconnected by communication links and segments for transporting databetween end nodes, such as personal computers and workstations, or otherdevices, such as sensors, etc. Many types of networks are available,with the types ranging from local area networks (LANs) to wide areanetworks (WANs). LANs typically connect the nodes over dedicated privatecommunications links located in the same general physical location, suchas a building or campus. WANs, on the other hand, typically connectgeographically dispersed nodes over long-distance communications links,such as common carrier telephone lines, optical lightpaths, synchronousoptical networks (SONET), or synchronous digital hierarchy (SDH) links,or Powerline Communications (PLC) such as IEEE 61334, IEEE P1901.2, andothers. The Internet is an example of a WAN that connects disparatenetworks throughout the world, providing global communication betweennodes on various networks. The nodes typically communicate over thenetwork by exchanging discrete frames or packets of data according topredefined protocols, such as the Transmission Control Protocol/InternetProtocol (TCP/IP). In this context, a protocol consists of a set ofrules defining how the nodes interact with each other. Computer networksmay be further interconnected by an intermediate network node, such as arouter, to extend the effective “size” of each network.

Smart object networks, such as sensor networks, in particular, are aspecific type of network having spatially distributed autonomous devicessuch as sensors, actuators, etc., that cooperatively monitor physical orenvironmental conditions at different locations, such as, e.g.,energy/power consumption, resource consumption (e.g., water/gas/etc. foradvanced metering infrastructure or “AMI” applications) temperature,pressure, vibration, sound, radiation, motion, pollutants, etc. Othertypes of smart objects include actuators, e.g., responsible for turningon/off an engine or perform any other actions. Sensor networks, a typeof smart object network, are typically shared-media networks, such aswireless or PLC networks. That is, in addition to one or more sensors,each sensor device (node) in a sensor network may generally be equippedwith a radio transceiver or other communication port such as PLC, amicrocontroller, and an energy source, such as a battery. Often, smartobject networks are considered field area networks (FANs), neighborhoodarea networks (NANs), personal area networks (PANs), etc. Generally,size and cost constraints on smart object nodes (e.g., sensors) resultin corresponding constraints on resources such as energy, memory,computational speed and bandwidth.

FIG. 1A is a schematic block diagram of an example computer network 100illustratively comprising nodes/devices, such as a plurality ofrouters/devices interconnected by links or networks, as shown. Forexample, customer edge (CE) routers 110 may be interconnected withprovider edge (PE) routers 120 (e.g., PE-1, PE-2, and PE-3) in order tocommunicate across a core network, such as an illustrative networkbackbone 130. For example, routers 110, 120 may be interconnected by thepublic Internet, a multiprotocol label switching (MPLS) virtual privatenetwork (VPN), or the like. Data packets 140 (e.g., traffic/messages)may be exchanged among the nodes/devices of the computer network 100over links using predefined network communication protocols such as theTransmission Control Protocol/Internet Protocol (TCP/IP), User DatagramProtocol (UDP), Asynchronous Transfer Mode (ATM) protocol, Frame Relayprotocol, or any other suitable protocol. Those skilled in the art willunderstand that any number of nodes, devices, links, etc. may be used inthe computer network, and that the view shown herein is for simplicity.

In some implementations, a router or a set of routers may be connectedto a private network (e.g., dedicated leased lines, an optical network,etc.) or a virtual private network (VPN), such as an MPLS VPN thanks toa carrier network, via one or more links exhibiting very differentnetwork and service level agreement characteristics. For the sake ofillustration, a given customer site may fall under any of the followingcategories:

1.) Site Type A: a site connected to the network (e.g., via a private orVPN link) using a single CE router and a single link, with potentially abackup link (e.g., a 3G/4G/LTE backup connection). For example, aparticular CE router 110 shown in network 100 may support a givencustomer site, potentially also with a backup link, such as a wirelessconnection.

2.) Site Type B: a site connected to the network using two MPLS VPNlinks (e.g., from different Service Providers), with potentially abackup link (e.g., a 3G/4G/LTE connection). A site of type B may itselfbe of different types:

2a.) Site Type B1: a site connected to the network using two MPLS VPNlinks (e.g., from different Service Providers), with potentially abackup link (e.g., a 3G/4G/LTE connection).

2b.) Site Type B2: a site connected to the network using one MPLS VPNlink and one link connected to the public Internet, with potentially abackup link (e.g., a 3G/4G/LTE connection). For example, a particularcustomer site may be connected to network 100 via PE-3 and via aseparate Internet connection, potentially also with a wireless backuplink.

2c.) Site Type B3: a site connected to the network using two linksconnected to the public Internet, with potentially a backup link (e.g.,a 3G/4G/LTE connection).

Notably, MPLS VPN links are usually tied to a committed service levelagreement, whereas Internet links may either have no service levelagreement at all or a loose service level agreement (e.g., a “GoldPackage” Internet service connection that guarantees a certain level ofperformance to a customer site).

3.) Site Type C: a site of type B (e.g., types B1, B2 or B3) but withmore than one CE router (e.g., a first CE router connected to one linkwhile a second CE router is connected to the other link), andpotentially a backup link (e.g., a wireless 3G/4G/LTE backup link). Forexample, a particular customer site may include a first CE router 110connected to PE-2 and a second CE router 110 connected to PE-3.

FIG. 1B illustrates an example of network 100 in greater detail,according to various embodiments. As shown, network backbone 130 mayprovide connectivity between devices located in different geographicalareas and/or different types of local networks. For example, network 100may comprise local/branch networks 160, 162 that include devices/nodes10-16 and devices/nodes 18-20, respectively, as well as a datacenter/cloud environment 150 that includes servers 152-154. Notably,local networks 160-162 and data center/cloud environment 150 may belocated in different geographic locations.

Servers 152-154 may include, in various embodiments, a networkmanagement server (NMS), a dynamic host configuration protocol (DHCP)server, a constrained application protocol (CoAP) server, an outagemanagement system (OMS), an application policy infrastructure controller(APIC), an application server, etc. As would be appreciated, network 100may include any number of local networks, data centers, cloudenvironments, devices/nodes, servers, etc.

In some embodiments, the techniques herein may be applied to othernetwork topologies and configurations. For example, the techniquesherein may be applied to peering points with high-speed links, datacenters, etc.

In various embodiments, network 100 may include one or more meshnetworks, such as an Internet of Things network. Loosely, the term“Internet of Things” or “IoT” refers to uniquely identifiable objects(things) and their virtual representations in a network-basedarchitecture. In particular, the next frontier in the evolution of theInternet is the ability to connect more than just computers andcommunications devices, but rather the ability to connect “objects” ingeneral, such as lights, appliances, vehicles, heating, ventilating, andair-conditioning (HVAC), windows and window shades and blinds, doors,locks, etc. The “Internet of Things” thus generally refers to theinterconnection of objects (e.g., smart objects), such as sensors andactuators, over a computer network (e.g., via IP), which may be thepublic Internet or a private network.

Notably, shared-media mesh networks, such as wireless or PLC networks,etc., are often on what is referred to as Low-Power and Lossy Networks(LLNs), which are a class of network in which both the routers and theirinterconnect are constrained: LLN routers typically operate withconstraints, e.g., processing power, memory, and/or energy (battery),and their interconnects are characterized by, illustratively, high lossrates, low data rates, and/or instability. LLNs are comprised ofanything from a few dozen to thousands or even millions of LLN routers,and support point-to-point traffic (between devices inside the LLN),point-to-multipoint traffic (from a central control point such at theroot node to a subset of devices inside the LLN), andmultipoint-to-point traffic (from devices inside the LLN towards acentral control point). Often, an IoT network is implemented with anLLN-like architecture. For example, as shown, local network 160 may bean LLN in which CE-2 operates as a root node for nodes/devices 10-16 inthe local mesh, in some embodiments.

In contrast to traditional networks, LLNs face a number of communicationchallenges. First, LLNs communicate over a physical medium that isstrongly affected by environmental conditions that change over time.Some examples include temporal changes in interference (e.g., otherwireless networks or electrical appliances), physical obstructions(e.g., doors opening/closing, seasonal changes such as the foliagedensity of trees, etc.), and propagation characteristics of the physicalmedia (e.g., temperature or humidity changes, etc.). The time scales ofsuch temporal changes can range between milliseconds (e.g.,transmissions from other transceivers) to months (e.g., seasonal changesof an outdoor environment). In addition, LLN devices typically uselow-cost and low-power designs that limit the capabilities of theirtransceivers. In particular, LLN transceivers typically provide lowthroughput. Furthermore, LLN transceivers typically support limited linkmargin, making the effects of interference and environmental changesvisible to link and network protocols. The high number of nodes in LLNsin comparison to traditional networks also makes routing, quality ofservice (QoS), security, network management, and traffic engineeringextremely challenging, to mention a few.

FIG. 2 is a schematic block diagram of an example node/device 200 thatmay be used with one or more embodiments described herein, e.g., as anyof the computing devices shown in FIGS. 1A-1B, particularly the PErouters 120, CE routers 110, nodes/device 10-20, servers 152-154 (e.g.,a network controller located in a data center, etc.), any othercomputing device that supports the operations of network 100 (e.g.,switches, etc.), or any of the other devices referenced below. Thedevice 200 may also be any other suitable type of device depending uponthe type of network architecture in place, such as IoT nodes, etc.Device 200 comprises one or more network interfaces 210, one or moreprocessors 220, and a memory 240 interconnected by a system bus 250, andis powered by a power supply 260.

The network interfaces 210 include the mechanical, electrical, andsignaling circuitry for communicating data over physical links coupledto the network 100. The network interfaces may be configured to transmitand/or receive data using a variety of different communicationprotocols. Notably, a physical network interface 210 may also be used toimplement one or more virtual network interfaces, such as for virtualprivate network (VPN) access, known to those skilled in the art.

The memory 240 comprises a plurality of storage locations that areaddressable by the processor(s) 220 and the network interfaces 210 forstoring software programs and data structures associated with theembodiments described herein. The processor 220 may comprise necessaryelements or logic adapted to execute the software programs andmanipulate the data structures 245. An operating system 242 (e.g., theInternetworking Operating System, or IOS®, of Cisco Systems, Inc.,another operating system, etc.), portions of which are typicallyresident in memory 240 and executed by the processor(s), functionallyorganizes the node by, inter alia, invoking network operations insupport of software processors and/or services executing on the device.These software processors and/or services may comprise routing process244 (e.g., routing services) and illustratively, a self learning network(SLN) process 248, as described herein, any of which may alternativelybe located within individual network interfaces.

It will be apparent to those skilled in the art that other processor andmemory types, including various computer-readable media, may be used tostore and execute program instructions pertaining to the techniquesdescribed herein. Also, while the description illustrates variousprocesses, it is expressly contemplated that various processes may beembodied as modules configured to operate in accordance with thetechniques herein (e.g., according to the functionality of a similarprocess). Further, while processes may be shown and/or describedseparately, those skilled in the art will appreciate that processes maybe routines or modules within other processes.

Routing process/services 244 include computer executable instructionsexecuted by processor 220 to perform functions provided by one or morerouting protocols, such as the Interior Gateway Protocol (IGP) (e.g.,Open Shortest Path First, “OSPF,” andIntermediate-System-to-Intermediate-System, “IS-IS”), the Border GatewayProtocol (BGP), etc., as will be understood by those skilled in the art.These functions may be configured to manage a forwarding informationdatabase including, e.g., data used to make forwarding decisions. Inparticular, changes in the network topology may be communicated amongrouters 200 using routing protocols, such as the conventional OSPF andIS-IS link-state protocols (e.g., to “converge” to an identical view ofthe network topology).

Notably, routing process 244 may also perform functions related tovirtual routing protocols, such as maintaining VRF instance, ortunneling protocols, such as for MPLS, generalized MPLS (GMPLS), etc.,each as will be understood by those skilled in the art. Also, EVPN,e.g., as described in the IETF Internet Draft entitled “BGP MPLS BasedEthernet VPN”<draft-ietf-12vpn-evpn>, introduce a solution formultipoint L2VPN services, with advanced multi-homing capabilities,using BGP for distributing customer/client media access control (MAC)address reach-ability information over the core MPLS/IP network.

SLN process 248 includes computer executable instructions that, whenexecuted by processor(s) 220, cause device 200 to perform anomalydetection functions as part of an anomaly detection infrastructurewithin the network. In general, anomaly detection attempts to identifypatterns that do not conform to an expected behavior. For example, inone embodiment, the anomaly detection infrastructure of the network maybe operable to detect network attacks (e.g., DDoS attacks, the use ofmalware such as viruses, rootkits, etc.). However, anomaly detection inthe context of computer networking typically presents a number ofchallenges: 1.) a lack of a ground truth (e.g., examples of normal vs.abnormal network behavior), 2.) being able to define a “normal” regionin a highly dimensional space can be challenging, 3.) the dynamic natureof the problem due to changing network behaviors/anomalies, 4.)malicious behaviors such as malware, viruses, rootkits, etc. may adaptin order to appear “normal,” and 5.) differentiating between noise andrelevant anomalies is not necessarily possible from a statisticalstandpoint, but typically also requires domain knowledge.

Anomalies may also take a number of forms in a computer network: 1.)point anomalies (e.g., a specific data point is abnormal compared toother data points), 2.) contextual anomalies (e.g., a data point isabnormal in a specific context but not when taken individually), or 3.)collective anomalies (e.g., a collection of data points is abnormal withregards to an entire set of data points). Generally, anomaly detectionrefers to the ability to detect an anomaly that could be triggered bythe presence of malware attempting to access data (e.g., dataexfiltration), spyware, ransom-ware, etc. and/or non-malicious anomaliessuch as misconfigurations or misbehaving code. Particularly, an anomalymay be raised in a number of circumstances:

-   -   Security threats: the presence of a malware using unknown        attacks patterns (e.g., no static signatures) may lead to        modifying the behavior of a host in terms of traffic patterns,        graphs structure, etc. Machine learning processes may detect        these types of anomalies using advanced approaches capable of        modeling subtle changes or correlation between changes (e.g.,        unexpected behavior) in a highly dimensional space. Such        anomalies are raised in order to detect, e.g., the presence of a        0-day malware, malware used to perform data ex-filtration thanks        to a Command and Control (C2) channel, or even to trigger        (Distributed) Denial of Service (DoS) such as DNS reflection,        UDP flood, HTTP recursive get, etc. In the case of a (D) DoS,        although technical an anomaly, the term “DoS” is usually used.        SLN process 248 may detect malware based on the corresponding        impact on traffic, host models, graph-based analysis, etc., when        the malware attempts to connect to a C2 channel, attempts to        move laterally, or exfiltrate information using various        techniques.    -   Misbehaving devices: a device such as a laptop, a server of a        network device (e.g., storage, router, switch, printer, etc.)        may misbehave in a network for a number of reasons: 1.) a user        using a discovery tool that performs (massive) undesirable        scanning in the network (in contrast with a lawful scanning by a        network management tool performing device discovery), 2.) a        software defect (e.g. a switch or router dropping packet because        of a corrupted RIB/FIB or the presence of a persistent loop by a        routing protocol hitting a corner case).    -   Dramatic behavior change: the introduction of a new networking        or end-device configuration, or even the introduction of a new        application may lead to dramatic behavioral changes. Although        technically not anomalous, an SLN-enabled node having computed        behavioral model(s) may raise an anomaly when detecting a brutal        behavior change. Note that in such as case, although an anomaly        may be raised, a learning system such as SLN is expected to        learn the new behavior and dynamically adapts according to        potential user feedback.    -   Misconfigured devices: a configuration change may trigger an        anomaly: a misconfigured access control list (ACL), route        redistribution policy, routing policy, QoS policy maps, or the        like, may have dramatic consequences such a traffic black-hole,        QoS degradation, etc. SLN process 248 may advantageously        identify these forms of misconfigurations, in order to be        detected and fixed.

In various embodiments, SLN process 248 may utilize machine learningtechniques, to perform anomaly detection in the network. In general,machine learning is concerned with the design and the development oftechniques that take as input empirical data (such as network statisticsand performance indicators), and recognize complex patterns in thesedata. One very common pattern among machine learning techniques is theuse of an underlying model M, whose parameters are optimized forminimizing the cost function associated to M, given the input data. Forinstance, in the context of classification, the model M may be astraight line that separates the data into two classes (e.g., labels)such that M=a*x+b*y+c and the cost function would be the number ofmisclassified points. The learning process then operates by adjustingthe parameters a,b,c such that the number of misclassified points isminimal. After this optimization phase (or learning phase), the model Mcan be used very easily to classify new data points. Often, M is astatistical model, and the cost function is inversely proportional tothe likelihood of M, given the input data.

Computational entities that rely on one or more machine learningtechniques to perform a task for which they have not been explicitlyprogrammed to perform are typically referred to as learning machines. Inparticular, learning machines are capable of adjusting their behavior totheir environment. For example, a learning machine may dynamically makefuture predictions based on current or prior network measurements, maymake control decisions based on the effects of prior control commands,etc.

For purposes of anomaly detection in a network, a learning machine mayconstruct a model of normal network behavior, to detect data points thatdeviate from this model. For example, a given model (e.g., a supervised,un-supervised, or semi-supervised model) may be used to generate andreport anomaly scores to another device. Example machine learningtechniques that may be used to construct and analyze such a model mayinclude, but are not limited to, nearest neighbor (NN) techniques (e.g.,k-NN models, replicator NN models, etc.), statistical techniques (e.g.,Bayesian networks, etc.), clustering techniques (e.g., k-means, etc.),neural networks (e.g., reservoir networks, artificial neural networks,etc.), support vector machines (SVMs), or the like.

One class of machine learning techniques that is of particular use inthe context of anomaly detection is clustering. Generally speaking,clustering is a family of techniques that seek to group data accordingto some typically predefined notion of similarity. For instance,clustering is a very popular technique used in recommender systems forgrouping objects that are similar in terms of people's taste (e.g.,because you watched X, you may be interested in Y, etc.). Typicalclustering algorithms are k-means, density based spatial clustering ofapplications with noise (DBSCAN) and mean-shift, where a distance to acluster is computed with the hope of reflecting a degree of anomaly(e.g., using a Euclidian distance and a cluster based local outlierfactor that takes into account the cluster density).

Replicator techniques may also be used for purposes of anomalydetection. Such techniques generally attempt to replicate an input in anunsupervised manner by projecting the data into a smaller space (e.g.,compressing the space, thus performing some dimensionality reduction)and then reconstructing the original input, with the objective ofkeeping the “normal” pattern in the low dimensional space. Exampletechniques that fall into this category include principal componentanalysis (PCA) (e.g., for linear models), multi-layer perceptron (MLP)ANNs (e.g., for non-linear models), and replicating reservoir networks(e.g., for non-linear models, typically for time series).

According to various embodiments, SLN process 248 may also usegraph-based models for purposes of anomaly detection. Generallyspeaking, a graph-based model attempts to represent the relationshipsbetween different entities as a graph of nodes interconnected by edges.For example, ego-centric graphs have been used to represent therelationship between a particular social networking profile and theother profiles connected to it (e.g., the connected “friends” of a user,etc.). The patterns of these connections can then be analyzed forpurposes of anomaly detection. For example, in the social networkingcontext, it may be considered anomalous for the connections of aparticular profile not to share connections, as well. In other words, aperson's social connections are typically also interconnected. If nosuch interconnections exist, this may be deemed anomalous.

An example self learning network (SLN) infrastructure that may be usedto detect network anomalies is shown in FIG. 3, according to variousembodiments. Generally, network devices may be configured to operate aspart of an SLN infrastructure to detect, analyze, and/or mitigatenetwork anomalies such as network attacks (e.g., by executing SLNprocess 248). Such an infrastructure may include certain network devicesacting as distributed learning agents (DLAs) and one or moresupervisory/centralized devices acting as a supervisory and controlagent (SCA). A DLA may be operable to monitor network conditions (e.g.,router states, traffic flows, etc.), perform anomaly detection on themonitored data using one or more machine learning models, reportdetected anomalies to the SCA, and/or perform local mitigation actions.Similarly, an SCA may be operable to coordinate the deployment andconfiguration of the DLAs (e.g., by downloading software upgrades to aDLA, etc.), receive information from the DLAs (e.g., detectedanomalies/attacks, compressed data for visualization, etc.), provideinformation regarding a detected anomaly to a user interface (e.g., byproviding a webpage to a display, etc.), and/or analyze data regarding adetected anomaly using more CPU intensive machine learning processes.

One type of network attack that is of particular concern in the contextof computer networks is a Denial of Service (DoS) attack. In general,the goal of a DoS attack is to prevent legitimate use of the servicesavailable on the network. For example, a DoS jamming attack mayartificially introduce interference into the network, thereby causingcollisions with legitimate traffic and preventing message decoding. Inanother example, a DoS attack may attempt to overwhelm the network'sresources by flooding the network with requests (e.g., SYN flooding,sending an overwhelming number of requests to an HTTP server, etc.), toprevent legitimate requests from being processed. A DoS attack may alsobe distributed, to conceal the presence of the attack. For example, adistributed DoS (DDoS) attack may involve multiple attackers sendingmalicious requests, making it more difficult to distinguish when anattack is underway. When viewed in isolation, a particular one of such arequest may not appear to be malicious. However, in the aggregate, therequests may overload a resource, thereby impacting legitimate requestssent to the resource.

Botnets represent one way in which a DDoS attack may be launched againsta network. In a botnet, a subset of the network devices may be infectedwith malicious software, thereby allowing the devices in the botnet tobe controlled by a single master. Using this control, the master canthen coordinate the attack against a given network resource.

DoS attacks are relatively easy to detect when they are brute-force(e.g. volumetric), but, especially when highly distributed, they may bedifficult to distinguish from a flash-crowd (e.g., an overload of thesystem due to many legitimate users accessing it at the same time). Thisfact, in conjunction with the increasing complexity of performedattacks, makes the use of “classic” (usually threshold-based) techniquesuseless for detecting them. However, machine learning techniques maystill be able to detect such attacks, before the network or servicebecomes unavailable. For example, some machine learning approaches mayanalyze changes in the overall statistical behavior of the networktraffic (e.g., the traffic distribution among flow flattens when a DDoSattack based on a number of microflows happens). Other approaches mayattempt to statistically characterizing the normal behaviors of networkflows or TCP connections, in order to detect significant deviations.Classification approaches try to extract features of network flows andtraffic that are characteristic of normal traffic or malicious traffic,constructing from these features a classifier that is able todifferentiate between the two classes (normal and malicious).

As shown in FIG. 3, routers CE-2 and CE-3 may be configured as DLAs andserver 152 may be configured as an SCA, in one implementation. In such acase, routers CE-2 and CE-3 may monitor traffic flows, router states(e.g., queues, routing tables, etc.), or any other conditions that maybe indicative of an anomaly in network 100. As would be appreciated, anynumber of different types of network devices may be configured as a DLA(e.g., routers, switches, servers, blades, etc.) or as an SCA.

Assume, for purposes of illustration, that CE-2 acts as a DLA thatmonitors traffic flows associated with the devices of local network 160(e.g., by comparing the monitored conditions to one or moremachine-learning models). For example, assume that device/node 10 sendsa particular traffic flow 302 to server 154 (e.g., an applicationserver, etc.). In such a case, router CE-2 may monitor the packets oftraffic flow 302 and, based on its local anomaly detection mechanism,determine that traffic flow 302 is anomalous. Anomalous traffic flowsmay be incoming, outgoing, or internal to a local network serviced by aDLA, in various cases.

In some cases, traffic 302 may be associated with a particularapplication supported by network 100. Such applications may include, butare not limited to, automation applications, control applications, voiceapplications, video applications, alert/notification applications (e.g.,monitoring applications), communication applications, and the like. Forexample, traffic 302 may be email traffic, HTTP traffic, trafficassociated with an enterprise resource planning (ERP) application, etc.

In various embodiments, the anomaly detection mechanisms in network 100may use Internet Behavioral Analytics (IBA). In general, IBA refers tothe use of advanced analytics coupled with networking technologies, todetect anomalies in the network. Although described later with greaterdetails, the ability to model the behavior of a device (networkingswitch/router, host, etc.) will allow for the detection of malware,which is complementary to the use of a firewall that uses staticsignatures. Observing behavioral changes (e.g., a deviation from modeledbehavior) thanks to aggregated flows records, deep packet inspection,etc., may allow detection of an anomaly such as an horizontal movement(e.g. propagation of a malware, etc.), or an attempt to performinformation exfiltration.

FIG. 4 illustrates an example distributed learning agent (DLA) 400 ingreater detail, according to various embodiments. Generally, a DLA maycomprise a series of modules hosting sophisticated tasks (e.g., as partof an overall SLN process 248). Generally, DLA 400 may communicate withan SCA (e.g., via one or more northbound APIs 402) and any number ofnodes/devices in the portion of the network associated with DLA 400(e.g., via APIs 420, etc.).

In some embodiments, DLA 400 may execute a Network Sensing Component(NSC) 416 that is a passive sensing construct used to collect a varietyof traffic record inputs 426 from monitoring mechanisms deployed to thenetwork nodes. For example, traffic record inputs 426 may include Cisco™Netflow records, application identification information from a Cisco™Network Based Application Recognition (NBAR) process or anotherapplication-recognition mechanism, administrative information from anadministrative reporting tool (ART), local network state informationservice sets, media metrics, or the like.

Furthermore, NSC 416 may be configured to dynamically employ Deep PacketInspection (DPI), to enrich the mathematical models computed by DLA 400,a critical source of information to detect a number of anomalies. Alsoof note is that accessing control/data plane data may be of utmostimportance, to detect a number of advanced threats such as dataexfiltration. NSC 416 may be configured to perform data analysis anddata enhancement (e.g., the addition of valuable information to the rawdata through correlation of different information sources). Moreover,NSC 416 may compute various networking based metrics relevant for theDistributed Learning Component (DLC) 408, such as a large number ofstatistics, some of which may not be directly interpretable by a human.

In some embodiments, DLA 400 may also include DLC 408 that may perform anumber of key operations such as any or all of the following:computation of Self Organizing Learning Topologies (SOLT), computationof “features” (e.g., feature vectors), advanced machine learningprocesses, etc., which DLA 400 may use in combination to perform aspecific set of tasks. In some cases, DLC 408 may include areinforcement learning (RL) engine 412 that uses reinforcement learningto detect anomalies or otherwise assess the operating conditions of thenetwork. Accordingly, RL engine 412 may maintain and/or use any numberof communication models 410 that model, e.g., various flows of trafficin the network. In further embodiments, DLC 408 may use any other formof machine learning techniques, such as those described previously(e.g., supervised or unsupervised techniques, etc.). For example, in thecontext of SLN for security, DLC 408 may perform modeling of traffic andapplications in the area of the network associated with DLA 400. DLC 408can then use the resulting models 410 to detect graph-based and otherforms of anomalies (e.g., by comparing the models with current networkcharacteristics, such as traffic patterns. The SCA may also send updates414 to DLC 408 to update model(s) 410 and/or RL engine 412 (e.g., basedon information from other deployed DLAs, input from a user, etc.).

When present, RL engine 412 may enable a feed-back loop between thesystem and the end user, to automatically adapt the system decisions tothe expectations of the user and raise anomalies that are of interest tothe user (e.g., as received via a user interface of the SCA). In oneembodiment, RL engine 412 may receive a signal from the user in the formof a numerical reward that represents for example the level of interestof the user related to a previously raised event. Consequently the agentmay adapt its actions (e.g. search for new anomalies), to maximize itsreward over time, thus adapting the system to the expectations of theuser. More specifically, the user may optionally provide feedback thanksto a lightweight mechanism (e.g., ‘like’ or ‘dislike’) via the userinterface.

In some cases, DLA 400 may include a threat intelligence processor (TIP)404 that processes anomaly characteristics so as to further assess therelevancy of the anomaly (e.g. the applications involved in the anomaly,location, scores/degree of anomaly for a given model, nature of theflows, or the like). TIP 404 may also generate or otherwise leverage amachine learning-based model that computes a relevance index. Such amodel may be used across the network to select/prioritize anomaliesaccording to the relevancies.

DLA 400 may also execute a Predictive Control Module (PCM) 406 thattriggers relevant actions in light of the events detected by DLC 408. Inorder words, PCM 406 is the decision maker, subject to policy. Forexample, PCM 406 may employ rules that control when DLA 400 is to sendinformation to the SCA (e.g., alerts, predictions, recommended actions,trending data, etc.) and/or modify a network behavior itself. Forexample, PCM 406 may determine that a particular traffic flow should beblocked (e.g., based on the assessment of the flow by TIP 404 and DLC408) and an alert sent to the SCA.

Network Control Component (NCC) 418 is a module configured to triggerany of the actions determined by PCM 406 in the network nodes associatedwith DLA 400. In various embodiments, NCC 418 may communicate thecorresponding instructions 422 to the network nodes using APIs 420(e.g., DQoS interfaces, ABR interfaces, DCAC interfaces, etc.). Forexample, NCC 418 may send mitigation instructions 422 to one or morenodes that instruct the receives to reroute certain anomalous traffic,perform traffic shaping, drop or otherwise “black hole” the traffic, ortake other mitigation steps. In some embodiments, NCC 418 may also beconfigured to cause redirection of the traffic to a “honeypot” devicefor forensic analysis. Such actions may be user-controlled, in somecases, through the use of policy maps and other configurations. Notethat NCC 418 may be accessible via a very flexible interface allowing acoordinated set of sophisticated actions. In further embodiments, API(s)420 of NCC 418 may also gather/receive certain network data 424 from thedeployed nodes such as Cisco™ OnePK information or the like.

The various components of DLA 400 may be executed within a container, insome embodiments, that receives the various data records and otherinformation directly from the host router or other networking device.Doing so prevents these records from consuming additional bandwidth inthe external network. This is a major advantage of such a distributedsystem over centralized approaches that require sending large amount oftraffic records. Furthermore, the above mechanisms afford DLA 400additional insight into other information such as control plane packetand local network states that are only available on premise. Note alsothat the components shown in FIG. 4 may have a low footprint, both interms of memory and CPU. More specifically, DLA 400 may use lightweighttechniques to compute features, identify and classify observation data,and perform other functions locally without significantly impacting thefunctions of the host router or other networking device.

As noted above, an anomaly detector process may rely on the notion ofstatistical likelihood to calculate an anomaly score that can be used,in turn, to determine whether a specific event is anomalous. Inparticular, an anomaly detector may operate by modeling a distributionf(X) of historical data, where X is a so-called feature vector in R^(n)whose dimensions are the n features that quantify different networkbehaviors. Then, each new event i can be scored as the posteriorprobability p=P(Xi|X₁, X₂, . . . , X_(i-1)). That is, an anomalydetector may look to the probability that the feature vector X, isobserved in the network, given previous observations X₁ to X_(i-1). Forvery low values of p, the anomaly detector may raise an anomaly and,typically, the higher the index i, the higher the “significance” of theanomaly. However, the notion of significance is often overlooked due tosystem limitations.

In one example, consider the case in which an anomaly detection systemobserves a 10 MB sized upload after seeing a plurality of 1 KB sizeduploads. If the number of observed 1 KB uploads is large (e.g., onemillion) and the number of observed 10 MB sized uploads is small (e.g.,ten), the system may consider the 10 MB upload as more anomalous.However, this does not indicate the actual significance of the event.For example, assume that the 1 KB uploads are DNS uploads from a groupof hosts A. Suddenly, two distinct hosts in A perform a 10 MB and a 1 MBupload, respectively. From a purely statistical sense, both events arejust as unlikely. However, from a networking standpoint, one is muchmore significant; that is, the extent of the data leak.

Some statistical models may capture the magnitude of a deviation quiteeasily. However, there are many more domain-specific biases that areal-life anomaly detection system must implement to yield relevantevents. In particular, there are relationships between features andprotocols that must be accounted for in the scoring process. Notably, alarge deviation in the number of bytes may be quite benign for someprotocols (e.g., HTTP), but may be extremely significant/critical forothers (e.g., Syslog). Similarly, anomalies related to flow duration maybe a major concern for some protocols and not for others (e.g., controlplane with keep-alive).

Score Boosting Strategies for Capturing Domain-Specific Biases inAnomaly Detection Systems

The techniques herein introduce a series of mechanisms that enable theenrichment of a distributed anomaly detection system by specificallyboosting the score of anomalous events that match certaindomain-specific criteria. In some aspects, these mechanisms allow forthe propagation of input at the edge for third-party security appliances(e.g., via threat intelligence feeds), an end user (e.g., via a userinterface), and/or domain experts (e.g., via the upload of pre-computedrules or statistical models).

Specifically, according to one or more embodiments of the disclosure asdescribed in detail below, a device in a network detects an anomaly inthe network using an anomaly detector. The anomaly corresponds to ananomalous behavior exhibited by one or more nodes in the network. Thedevice computes an anomaly score for the anomaly that represents ameasure of the anomalous behavior. The device adjusts the anomaly scoreusing a boost score. The boost score is generated by a boosting functionthat accounts for domain-specific biases of the anomaly detector. Thedevice reports the anomaly to a supervisory device based on whether theadjusted anomaly score exceeds a reporting threshold.

Illustratively, the techniques described herein may be performed byhardware, software, and/or firmware, such as in accordance with the SLNprocess 248, which may include computer executable instructions executedby the processor 220 (or independent processor of interfaces 210) toperform functions relating to the techniques described herein, e.g., inconjunction with routing process 244.

Operationally, FIGS. 5A-5D illustrate an example of a DLA applying aboost score to an anomaly score, according to various embodiments. Oneaspect of the techniques herein introduces a boosting function B( ) thata DLA may use to adjust an anomaly score.

As shown in FIG. 5A, a DLA 400 a may execute a Distributed OptimalForwarder (DOF) 510 in conjunction with the processes described abovewith respect to DLA 400. Generally, DOF 510 is configured to determinewhether to notify a central controller (e.g., SCA 152) when DLA 400 adetects an anomaly. Notably, DOF 510 may store and apply boostingfunction B( ) to the anomaly scores of anomalies detected by DLA 400 a,to determine whether a particular anomaly is of great enoughsignificance to report the anomaly to SCA 152. In various embodiments,DOF 510 may be a stand-alone process or implemented as a sub-process ofanother DLA module, such as DLC 408.

Assume, for purposes of illustration, that DLA 400 a monitors thetraffic flows from any number of nodes/devices 502 (e.g., a firstthrough nth node/device) in the local domain or network of DLA 400 a. Asdescribed above, DLA 400 a may monitor the corresponding traffic flows504 (e.g., a first through nth traffic flow) associated with hostnodes/devices 502 to gather features for construction of a featurevector. Example features may include, but are not limited to, the timesof traffic flows 504, the durations of traffic flows 504, the size oftraffic flows 504, the source and/or destinations of traffic flows 504,the applications associated with traffic flows 504, the protocolsassociated with traffic flows 504, combinations thereof, or any othertraffic information that may indicate a potential network anomaly. Forexample, assume that DLA 400 a determines that traffic flow 504 a fromhost node 502 a is anomalous, based on the gathered characteristics oftraffic flow 504 a.

As shown in FIG. 5B, when DLA 400 a detects an anomaly, DOF 510 maycapture each anomaly and adjust/boost the corresponding anomaly score ofthe anomaly using boosting function B( ). In turn, DLA 400 a maydetermine whether to report the anomaly based on the adjusted/boostedscore exceeding a reporting threshold and/or other factors, as shown inFIG. 5C. For example, DLA 400 a may take into account the scores ofprevious anomalies when determining the reporting threshold. In furtherembodiments, DOF 510 may employ a reporting “budget” to determine whichanomalies to report to SCA 152 in a given timeframe. For example, DOF510 may constrain the cumulative adjusted scores of the reportedanomalies in a given time period, to further limit which anomalies arereported and conserve network resources. Of course, the higher theadjusted score, the higher the chances the anomaly is forwarded to thecentral controller. Also, reported anomaly scores are often mapped intoseverity classes (e.g. low, medium, high) on controllers and/or aSecurity Information and Event Management (SIEM) system, making thedynamic adjusting of anomaly scores of utmost importance to anomalydetection systems.

As shown in FIG. 5D, if DLA 400 a determines that the anomaly score forthe anomalous traffic flow 504 a is such that reporting is requiredafter adjustment using a boost score from the boosting function, DLA 400a may send an anomaly notification 506 to SCA 152. Notification 506 mayinclude any or all of the information regarding the detected anomaly, invarious embodiments. For example, notification 506 may simply identifythat traffic flow 504 a is anomalous or may include the full set offeature vector(s) that resulted in the raised anomaly.

The boosting function B( ) may take many forms, one of which is simply aset of rules that multiply the anomaly score by a given factor when theyare matched. An example of the function B( ) is illustrated below:

function B(anomaly) { score = anomaly.score for (rule <− ruleset) { if(rule.match(anomaly)) { score = score * rule.factor } } return score }

Such rules may address, for example, any domain-specific biases of theanomaly detector. In other words, while a particular traffic flow orother behavior may be anomalous in comparison to other traffic orbehaviors in the domain, the traffic or other behavior may be of littlesignificance from a global perspective. Example rules may include, butare not limited to, rules that pertain to the traffic size, traffictiming (e.g., start, end, or duration of a traffic flow), applicationsor protocols associated with the traffic, or the like.

In another embodiment, the system may learn the function B( ) from thefeedback of experts. In this case, sample anomalies obtained fromprevious runs of the system are rated by a series of experts and thesystem uses a regression process (e.g., decision trees, neural networks,deep learning) to learn the relationship between the properties of theanomaly (e.g., the type of traffic, the type and magnitude of thedeviation, the time of day, etc.) and the score that would be given byan expert user. In other words, the boosting function B( ) may use amachine learning process that is trained with feedback from a networkadministrator or other user.

In yet another embodiment, the system may learn the function B( ) fromthe aggregation of threat intelligence reports. In this case, the systemmay generate feature vectors that correspond to potential anomalies fromthe description of known attacks and/or trends and they are used totrain classification processes, such as deep neural networks. Thesetypes of processes are well-suited to the aggregation of veryheterogeneous and complex sources of data, as they are able to constructhierarchical representations of features. The resulting classifier canthen be used to perform score boosting, based on the criticality of thepattern. For example, the function B( ) may take the form:

function B(anomaly) { fs = construct_features(anomaly) class =predict_class(fs) if (class is not “unknown”) { return anomaly.score *class.factor } }

In the above example, assume that each class of attack has awell-defined multiplicative factor, but this factor can be made aconstant (e.g., 2.0). Other constants may be used in other embodiments.Such classifiers can be computed on the controller (e.g., SCA 152) andthen downloaded to the edge of the network (e.g., to DLA 400 a), using acustom message.

In further embodiments, DOF 510 may be in charge of further modifyingthe function B( ) according to local criteria. Notably, DOF 510 mayfurther boost/adjust the anomaly score according to local historicaldata for the anomaly. For example, DLA 400 a may maintain a local indexfor each host according to the number of anomalies they have beeninvolved in for the past X hours or other time period. DLA 400 a maythen apply a multiplying factor to boost scores of anomalies accordingto the history for the host. In some embodiments, the factor maydecrease with time based on a time function (e.g., a linear orexponentially decreasing time function).

In another embodiment, DOF 510 may use negative boosting, in whichboosting function B( ) may reduce the anomaly score instead ofincreasing the score. This can be particularly useful for suppressingevents that are known, yet legitimate, statistical deviations. In somecases, one might want to use negative boosting for known attack patternsthat are easy to capture using conventional approaches (e.g.,firewalls), so as to allow emergent, zero-day malware to be reportedmore easily, especially if they exhibit extremely subtle behavior from astatistical standpoint. In this case, SCA 152 may decide whether or notto use negative boosting in the various DLAs based on their location inthe network, and whether the traffic they monitor is also undersurveillance by conventional appliances.

Another aspect introduced herein is a Score Boosting Manager (SBM) 520,which may be located in the central controller, such as SCA 152. FIGS.6A-6D illustrate an example of the distribution of a boosting functionin a network, according to various embodiments.

Generally, SBM 520 is responsible for computing and pushing the boostingfunction B( ) to each DLA. For example, as shown in FIG. 6A, assume thatSCA 152 oversees any number of DLAs 400 (e.g., a first through nth DLA).In such a case, SBM 520 on SCA 152 may aggregate information from anynumber of sources to construct boosting function B( ). For example, ifB( ) takes the form of a set of rules, SBM 520 may offer an applicationprogram interface (API) and/or a Graphical User Interface (GUI) 604 tothe user for editing those rules. Notably, a user operating anadministrative device 602 or SCA 152 itself may manipulate GUI 604 todefine the rules for the boosting function B( ). In turn, GUI 604 maysend the defined rule information 606 to SBM 520 for processing.

When the function B( ) is learned, SBM 520 may be responsible forcollecting training data, performing the training of the model as wellas its cross-validation, and then, if satisfactory, pushing the resultto any or all of DLAs 400 a-400 n.

In one embodiment, SBM 520 may subscribe to threat intelligence feedsand construct a training set from the obtained Indicators of Compromise(IOCs), as shown in FIG. 6B. Generally, threat intelligence feeds arefeeds from a threat intelligence service that provide informationregarding known threats (e.g., potentially malicious addresses, attackpatterns, etc.). For example, threat intelligence service 608 mayprovide threat intelligence feed 610 to SBM 520, as illustrated in FIG.6B. An example function for training a boosting function using an IOC isillustrated below:

classifier = deep_neural_net( ) training_set = get_initial_dataset( )for (feed <− sources) { for (ioc <− feed) { x = construct_features(ioc)add (x, ioc.attack_type) to training_set } }classifier.train(training_set)

Note that the function get_initial_dataset( ) may return a training setcomposed of “unknown” examples, that is, feature vectors of anomaliesthat do not correspond to any clear attack pattern.

As shown in FIG. 6C, SBM 520 may generate the boost function B( ) usingany or all of the techniques described herein. For example, SBM 520 mayuse rules provided via a GUI and/or IOCs from a threat intelligencefeed, to generate the boost function. In further embodiments, SBM 520may train the boosting function using feedback from the GUI regardingthe significance of reported anomalies. In turn, SBM 520 may train amachine learning model to predict whether a new set of feature vectorswould also be labeled as significant to the user.

In FIG. 6D, SBM 520 may distribute the generated boosting function 612to any or all of DLAs 400 a-400 n. In turn, the receiving DLA may usethe boosting function to calculate boost scores for any anomaliesdetected by the DLA, to control its anomaly reporting to SCA 152.

FIG. 7 illustrates an example simplified procedure for employing a scoreboosting strategy to capture domain-specific biases in an anomalydetection system, in accordance with embodiments herein. Procedure 700may be performed, for example, by a device in a network executing storedinstructions (e.g., SLN process 248, etc.). For example, a DLA at theedge of a local domain or network may perform procedure 700 as part ofits anomaly detection functions. Procedure 700 starts at step 705 andcontinues on to step 710 where, as described in greater detail above,the device detects an anomaly in the network. For example, the devicemay execute one or more machine learning or statistics-based anomalydetectors to analyze network traffic or other networkbehaviors/characteristics and identify when an anomaly exists.

At step 715, as detailed above, the device may compute an anomaly scorefor the detected anomaly. Generally, the anomaly score may represent thedegree of the anomalous behavior. For example, if the anomaly detectoruses statistics to detect anomalies, the anomaly score may be based onthe computed probability of the device observing a set of features(e.g., network characteristics/behaviors), in view of previouslyobserved features. In other words, the anomaly score may represent howanomalous the behavior is in comparison to the previous behaviors of thenetwork.

At step 720, the device may adjust the anomaly score using a boostscore, as described in greater detail above. In various embodiments, theboost score is generated by a boosting function that accounts fordomain-specific biases of the anomaly detector. For example, theboosting function may comprise a set of rules that relate to the type,application, and/or protocol of a traffic flow, timing information forthe traffic flow (e.g., start, end, and/or duration), or otherinformation that may control the resulting boost score for a detectedanomaly. In another embodiment, the boosting function may comprise amachine learning classifier that was trained using training data fromone or more threat intelligence feeds. In a further embodiment, theboosting function may be a machine learning process trained usingfeedback from a user interface regarding the significance of otherreported anomalies (e.g., a regression-based process, etc.). In variouscases, the device may adjust the anomaly score by multiplying, adding,etc. the boost score to it. In some cases, the boost score, whenapplied, may even lower the anomaly score.

At step 725, as detailed above, the device may report the detectedanomaly to a supervisory device (e.g., an SCA, etc.), based on whetherthe adjusted anomaly score exceeds a reporting threshold. For example,if the anomaly score itself is very high due and the boost score is verylow, this may prevent the device from reporting the anomaly despite theoriginally high anomaly score. Conversely, the device may report ananomaly with a low anomaly score but a very high boost score, since themildly anomalous behavior is of high significance (e.g., the anomalyrelates to a particular protocol or application of interest, etc.). Insome embodiments, the device may further base the reporting on prioranomalies (e.g., using an index of previously detected anomalies), areporting budget, or other mechanisms to control anomaly reporting.Procedure 700 then ends at step 730.

It should be noted that while certain steps within procedure 700 may beoptional as described above, the steps shown in FIG. 7 are merelyexamples for illustration, and certain other steps may be included orexcluded as desired. Further, while a particular order of the steps isshown, this ordering is merely illustrative, and any suitablearrangement of the steps may be utilized without departing from thescope of the embodiments herein.

The techniques described herein, therefore, allow an anomaly detectionsystem to alleviate critical problems associated with anomalous eventsthat are statistically “surprising,” but are not “relevant” from asecurity standpoint. In some aspects the techniques herein allow for thesuppression of these events to the benefit of other relevant events.Notably, certain aspects of the techniques use a domain specificboosting function to modify the score of a detected anomaly. Inaddition, the techniques use supervised learning to extract domainspecific knowledge.

While there have been shown and described illustrative embodiments forcapturing domain-specific biases in anomaly detection systems, it is tobe understood that various other adaptations and modifications may bemade within the spirit and scope of the embodiments herein. For example,while certain embodiments are described herein with respect to usingcertain models for purposes of anomaly detection, the models are notlimited as such and may be used for other functions, in otherembodiments.

The foregoing description has been directed to specific embodiments. Itwill be apparent, however, that other variations and modifications maybe made to the described embodiments, with the attainment of some or allof their advantages. For instance, it is expressly contemplated that thecomponents and/or elements described herein can be implemented assoftware being stored on a tangible (non-transitory) computer-readablemedium (e.g., disks/CDs/RAM/EEPROM/etc.) having program instructionsexecuting on a computer, hardware, firmware, or a combination thereof.Accordingly this description is to be taken only by way of example andnot to otherwise limit the scope of the embodiments herein. Therefore,it is the object of the appended claims to cover all such variations andmodifications as come within the true spirit and scope of theembodiments herein.

What is claimed is:
 1. A method comprising: detecting, by a device in anetwork, an anomaly in the network using an anomaly detector, whereinthe anomaly corresponds to an anomalous behavior exhibited by one ormore nodes in the network; computing, by the device, an anomaly scorefor the anomaly that represents a measure of the anomalous behavior;once the anomaly score has been computed, adjusting, by the device, theanomaly score using a boost score, wherein the boost score is generatedby a boosting function that accounts for domain-specific biases of theanomaly detector and multiplies the anomaly score by a factor based thedomain specific biases of the anomaly detector; and reporting, by thedevice, the anomaly to a supervisory device based on whether theadjusted anomaly score exceeds a reporting threshold.
 2. The method asin claim 1, wherein the boosting function comprises a machine learningclassifier that was trained using training data from one or more threatintelligence feeds.
 3. The method as in claim 1, wherein the boostingfunction comprises a set of rules, wherein at least one of the rulesspecifies an application type and the application type matches that oftraffic associated with the detected anomaly.
 4. The method as in claim1, wherein the boosting function comprises a set of rules, wherein atleast one of the rules specifies a network protocol and the networkprotocol matches that of traffic associated with the detected anomaly.5. The method as in claim 1, wherein the boosting function comprises aregression-based machine learning process trained using feedback from auser interface.
 6. The method as in claim 1, further comprising:receiving, at the device, the boosting function from the supervisorydevice.
 7. The method as in claim 1, wherein the boost score decreasesthe anomaly score.
 8. The method as in claim 1, further comprising:maintaining, by the device, an index of anomalies detected by the devicewithin a given timeframe, wherein the boost score is based in part on aquantity of anomalies in the index of anomalies.
 9. An apparatus,comprising: one or more network interfaces to communicate with anetwork; a processor coupled to the one or more network interfaces andconfigured to execute one or more processes; and a memory configured tostore a process executable by the processor, the process when executedoperable to: detect an anomaly in the network using an anomaly detector,wherein the anomaly corresponds to an anomalous behavior exhibited byone or more nodes in the network; compute an anomaly score for theanomaly that represents a measure of the anomalous behavior; once theanomaly score has been computed, adjust the anomaly score using a boostscore, wherein the boost score is generated by a boosting function thataccounts for domain-specific biases of the anomaly detector andmultiplies the anomaly score by a factor based the domain specificbiases of the anomaly detector; and report the anomaly to a supervisorydevice based on whether the adjusted anomaly score exceeds a reportingthreshold.
 10. The apparatus as in claim 9, wherein the boostingfunction comprises a set of rules, wherein at least one of the rulesspecifies a network protocol and the network protocol matches that oftraffic associated with the detected anomaly.
 11. The apparatus as inclaim 9, wherein the boosting function comprises a regression-basedmachine learning process trained using feedback from a user interface.12. The apparatus as in claim 9, wherein the process when executed isfurther operable to: receive the boosting function from the supervisorydevice.
 13. The apparatus as in claim 9, wherein the boost scoredecreases the anomaly score.
 14. The apparatus as in claim 9, whereinthe process when executed is further operable to: maintain an index ofanomalies detected by the device within a given timeframe, wherein theboost score is based in part on a quantity of anomalies in the index ofanomalies.
 15. The apparatus as in claim 14, wherein the process whenexecuted is further operable to: decrease the boost score using a linearor exponential time function.
 16. The apparatus as in claim 9, whereinthe boosting function comprises a machine learning classifier that wastrained using training data from one or more threat intelligence feeds.17. A tangible, non-transitory, computer-readable media having softwareencoded thereon, the software when executed by a device in a networkconfigured to: detect an anomaly in the network using an anomalydetector, wherein the anomaly corresponds to an anomalous behaviorexhibited by one or more nodes in the network; compute an anomaly scorefor the anomaly that represents a measure of the anomalous behavior;once the anomaly score has been computed, adjust the anomaly score usinga boost score, wherein the boost score is generated by a boostingfunction that accounts for domain-specific biases of the anomalydetector and multiplies the anomaly score by a factor based the domainspecific biases of the anomaly detector; and report the anomaly to asupervisory device based on whether the adjusted anomaly score exceeds areporting threshold.
 18. The tangible, non-transitory, computer-readablemedia as in claim 17, wherein the boosting function comprises a machinelearning classifier that was trained using training data from one ormore threat intelligence feeds.
 19. The tangible, non-transitory,computer-readable media as in claim 17, wherein the boosting functioncomprises a set of rules, wherein at least one of the rules specifies anapplication type or a network protocol and the application type or thenetwork protocol matches that of traffic associated with the detectedanomaly.
 20. The tangible, non-transitory, computer-readable media as inclaim 17, wherein the boosting function comprises a regression-basedmachine learning process trained using feedback from a user interface.